For CIFARs, we train the models for 300 epochs with a starting learning rate of \(0.1\)  decayed by a factor of 5 (baseline, ERL, Mixup) or 10 (LS, FL) at 150 and 225 epochs. For calibration approaches many of the respective hyper-parameters are borrowed from the original work. For TS we use the temperature of \(1.5\) . For MX, we use \(\alpha =0.2\)  based on the results provided by [1]}, [2]}. For LS, we use \(\epsilon =0.05\)  following the work of [3]} and [4]}. We employ the fixed gamma variant for FL with \(\gamma =3.0\) . The strength of the entropy regularizer in ERL is set to \(0.1\)  based on the experiments of [1]}.
